Knight Projects for the Year

I am installed in Miami for the academic year as a Visiting Knight Chair in the Journalism department; I've been busy (frantically, insanely busy) trying to put together class materials for the semester, grade stuff, produce talks and workshops, and keep up with Twitter.

As a nice benefit — or responsibility — I have project money to spend on activities or products that will improve the lives of the journalists of the future. Or of the now, if I do it right. Apart from some conference organization with Alberto Cairo, I'm thinking hard about how I'd like to spend that money. Here are a few things I tweeted about a week ago that I think would be of great benefit to data journalists, which don't yet exist fully:

"A few of my Wish List items for improving work, probably out of my project $ and scope:"

1. "A data-wrangler tool like Trifacta, easy to get/use."
   
2. "A customizable, comprehensive interactive vis lib with easy basics - like Vega 2 but maybe more baked? Vega in a year?"
   
3. "A non-programming tool for visualization creation that outputs code you can tweak. Lyra, basically, baked."
   
4. "A Shiny Server and similar paradigm for Python."
   
5. "HTMLwidgets for Python -- we need one ring to bind them, or something. Soooo many attempts to make notebook vis graphics."
   
6. "One more - tools/methods for making training and sharing entity recognizers easier. HUGE problem in text analysis."
   

A few of these tools are under active development in the University of Washington's Interactive Data Lab, particularly Vega and Lyra. (I recommend this video of Arvind Satyanaryan demoing Lyra at OpenVis Conf.) One, Trifacta, is a spin-off company and product from Jeff Heer (Director of the IDL) and student Sean Kandel, who created Data Wrangler. If you want to see some of the excellent tool future in the works at UW's IDL, Jeff Heer's keynote at OpenVis this year was outstanding.

And apparently there's more goodness in the works addressing my needs for IPython notebook interactive widgets in a sub-vega project on Github, pointed out by Rob Story), called ipython-vega right now. Also on the Python front, Rob Story suggests we might want to look at Pyxley from Stitchfix, but to me that still currently looks like a lot of programming and manual setup for a non-programmery analyst. Shiny apps are dead-simple for data analysts with a little gumption to throw up and share with folks right from their R Studio environment.

The future looks great about 5+ years out when all the grad students have finished and productized (or gotten significant coding support). But right now there is still a lot of pain, especially when you're trying to teach folks and recommend tools that are stable, documented, and tested (by people, not unit tests, although those too). Trifacta, of course, is not open-source. A competitor product, Alteryx, looks nice and has an academic license scheme but the non-academic version is $4K! Both for students and data journalists, enterprise level pricing for data wrangling tools is looking scary.

Aside on Entity Recognizers

Oh, a little note on the #6 item, entity recognition tools... Anyone who is trying to do named entity recognition (NER) in text files has a horrible slog getting good results. NER means things like looking up all the people, places, products, or companies in a text. It's hard because different strings are used to refer to the same things. To get results that are any good, especially on dynamic recent data (like news!), you need to train a recognizer with labeled text. (This is because the "out of the box" models and tools like Stanford NER etc. are almost always inadequate for what you really want.) The tools to do the labeling, and the labeling itself, pretty much suck. (Although I admit I haven't looked at the most recent one recommended to me by the Caerus folks.) I know a lot of grad students are suffering with this, when doing research on text in highly specific domains.

I'd love to see a marketplace for trained models customized for different domains, and easy-peasy tools for updating them and sharing improvements. I wish someone's NLP student would tackle this as a startup. Or, I suppose, I could do it with my project money and some help.

Instead, Text Analysis and Vis How-To's?

In the realm of things I can deliver that don't require a corporate team of developers, I'm thinking about doing an online repo ("book") of text analysis and visualization methods. This will be a combination of NLP and corpus analysis methods (in R and Python, I hope) as well as a handbook of visualization methods for text (with sample D3 code). The audience would be journalists with text to analyze, digital humanists with corpora, linguists wanting to get more visual with their work. Because my time is shockingly limited, I'll probably recruit an external helper with my project money to create code samples. If you've seen my epic collection of text vis on Pinterest and want to know "how do I make those?" I hope I'll be able to help you all.

How does this sound? Useful?

Any other ideas from folks out there? I'm chatting with my pals at Bocoup (Irene, Jim, Yannick) about other options for collaborations between us.

Local Workshops on Data Journalism Topics

One of my contributions to the local community at U of Miami is a series of workshops on topics hopefully of interest to data journalists (that I am qualified to teach). The first was a well-attended one on Excel Data Analysis (files here), and upcoming topics include:

• Excel Charts and Graphs
  
• Just What is Big Data (and Data Science) Anyway?
  
• Intro to Web Analytics: A/B Testing and Tracking
  
• Intro to Tableau
  
• Python and R: What Are They Good For?
  
• Text Mining with Very Little Programming
  
• Visualizing Network Data
  

I'd like to do one on command line data analysis, and some more on Python and R tools, but am not sure yet where the group wants to go. Stay tuned for more links!